A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Hild Aono Alexandre, Francisco Felipe Roberto, Moura Souza Livia, De Souza Gonçalves Paulo, Scaloppi Junior Erivaldo José, Le Guen Vincent, Fritsche-Neto Roberto, Gorjanc Gregor, Gonçalves Quiles Marcos, Pereira de Souza Anete. 2022. A divide-and-conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, 12:18023, 14 p.

https://doi.org/10.1038/s41598-022-20416-z

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Url - jeu de données - Entrepôt autre : https://www.ncbi.nlm.nih.gov/bioproject/PRJNA540286 / Url - jeu de données - Entrepôt autre : https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA541308

Résumé : Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci have been constructed and suggested as a tool for marker-assisted selection. Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

Mots-clés Agrovoc : Hevea brasiliensis, locus des caractères quantitatifs, apprentissage machine, marqueur génétique, génomique, sélection, amélioration des plantes, méthodologie, technique de prévision, amélioration génétique

Mots-clés géographiques Agrovoc : Brésil

Mots-clés libres : Rubber tree, Genomic prediction, Machine Learning

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

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